09-Apr-2021 | Market Research Store


Beef is one of the meat products consumed on a large scale across the globe. The consumption of meat after its prime time tends to make it tasteless and also increases the chances of health risks. The existing methods used to check the freshness of beef has few serious disadvantages that prevent its use in the public. The demand for time and skilled professionals for the chemical analysis or microbial population evaluations reduces its commercial utility. Additionally, the near infrared spectroscopy proves to be an expensive and highly advanced non-destructive approach. The researchers from Gwangju Institute of Science and Technology planned on using artificial intelligence to make a more cost-effective way to assess the freshness of beef. According to researchers Kyoobin Lee and Jae Gwan Kim, the team combined diffuse reflectance spectroscopy (DRS) with deep learning, to make a cost-effective optical technique.

DRS does not require calibration and could also be used to quantify part of the molecular composition of a sample using the spectrometer. The freshness of the beef was estimated by analyzing the diverse types of myoglobin in the meat. The myoglobin and its derivatives tend to change color when decomposing. The conversion of DRS measurements into myoglobin composition is inaccurate thereby insisting the use of deep learning.

The researchers used convolutional neural networks for learning from an earlier classified dataset to find hidden patterns in the data for classifying further novel inputs. The measuring of pH along with the DRS profiles provides a clear picture of the freshness of beef. The DRS data based on the pH values based DRS data along with the myoglobin estimations help train the algorithm better. These together helped decide the freshness of the beef samples. The non-destructive nature, low cost, and speed of this novel strategy turns it into a perfect option. The team is trying to build small, portable spectroscopic devices for assessing the beef freshness at home itself.